Are Education and Entrepreneurial Income Endogenous and Do Family Background Variables Make Sense as Instruments?: A Bayesian Analysis

Education is a well-known driver of (entrepreneurial) income. The measurement of its influence, however, suffers from endogeneity suspicion. For instance, ability and occupational choice are mentioned as driving both the level of (entrepreneurial) income and of education. Using instrumental variables can provide a way out. However, three questions remain: whether endogeneity is really present, whether it matters and whether the selected instruments make sense. Using Bayesian methods, we find that the relationship between education and entrepreneurial income is indeed endogenous and that the impact of endogeneity on the estimated relationship between education and income is sizeable. We do so using family background variables and show that relaxing the strict validity assumption of these instruments does not lead to strongly different results. This is an important finding because family background variables are generally strongly correlated with education and are available in most datasets. Our approach is applicable beyond the field of returns to education for income. It applies wherever endogeneity suspicion arises and the three questions become relevant.Education, income, entrepreneurship, self-employment, endogeneity, instrumental variables, Bayesian analysis, family background variables

Education and Entrepreneurial Choice: An Instrumental Variables Analysis

Education is argued to be an important driver of the decision to start a business. The measurement of its influence, however, is difficult since it is considered to be an endogenous variable. This study accounts for this endogeneity by using an instrumental variables approachand a data set of more than ten thousand individuals from 27 European countries and the US. Theeffect of education on the decision to become self-employed is found to be strongly positive,much higher than the estimated effect in case no instrumental variables are used. That is, thehigher the respondent's level of education, the greater the likelihood that he/she starts a business.Implications for entrepreneurship research and practice are discussed

Education and Entrepreneurial Choice: An Instrumental Variables Analysis

Education is argued to be an important driver of the decision to start a business. The measurement of its influence, however, is difficult since it is considered to be an endogenous variable. This study is the first to account for this endogeneity by using an instrumental variables approach. The effect of education on the decision to become self-employed is found to be strongly positive, much higher than the estimated effect in case no instrumental variables are used. That is, the higher the respondent's level of education, the greater the likelihood that he or she starts a business. Implications for method and practice are discussed

Family Background Variables as Instruments for Education in Income Regressions: A Bayesian Analysis

The validity of family background variables instrumenting education in income regressions has been much criticized. In this paper, we use data of the 2004 German Socio-Economic Panel and Bayesian analysis in order to analyze to what degree violations of the strong validity assumption affect the estimation results. We show that, in case of moderate direct effects of the instrument on the dependent variable, the results do not deviate much from the benchmark case of no such effect (perfect validity of the instrument). The size of the bias is in many cases smaller than the standard error of education’s estimated coefficient. Thus, the violation of the strict validity assumption does not necessarily lead to strongly different results when compared to the strict validity case. This provides confidence in the use of family background variables as instruments in income regressions

What Turns Knowledge into Innovative Products? The Role of Entrepreneurship and Knowledge Spillovers

The knowledge spillover theory of entrepreneurship seeks to explain the fundamentals and consequences of entrepreneurship with respect to economic performance. This paper uses the knowledge spillover theory to explain different innovation outcomes. We hypothesize that a high rate of entrepreneurship facilitates the process of turning knowledge into new-to-the-market innovation but has no effect on the relationship between knowledge and new-to-the-firm innovation. Our results using European country-level and pooled OLS, fixed- and random-effects regressions show that a high rate of entrepreneurship increases the chances that knowledge will become new-to-the-market innovation. The findings highlight the importance of Schumpeterian entrepreneurship in the process of the commercialization of knowledge. We discuss the implications for entrepreneurship and innovation policy

Are Education and Entrepreneurial Income Endogenous and do Family Background Variables make Sense as Instruments? A Bayesian Analysis

Education is a well-known driver of (entrepreneurial) income. The measurement of its influence, however, suffers from endogeneity suspicion. For instance, ability and occupational choice are mentioned as driving both the level of (entrepreneurial) income and of education. Using instrumental variables can provide a way out. However, three questions remain: whether endogeneity is really present, whether it matters and whether the selected instruments make sense. Using Bayesian methods, we find that the relationship between education and entrepreneurial income is indeed endogenous and that the impact of endogeneity on the estimated relationship between educa-tion and income is sizeable. We do so using family background variables and show that relaxing the strict validity assumption of these instruments does not lead to strongly different results. This is an important finding because family background variables are generally strongly correlated with education and are available in most datasets. Our approach is applicable beyond the field of returns to education for income. It applies wherever endogeneity suspicion arises and the three questions become relevant

Are Education and Entrepreneurial Income Endogenous and do Family Background Variables make Sense as Instruments? A Bayesian Analysis

Education is a well-known driver of (entrepreneurial) income. The measurement of its influence, however, suffers from endogeneity suspicion. For instance, ability and occupational choice are mentioned as driving both the level of (entrepreneurial) income and of education. Using instrumental variables can provide a way out. However, three questions remain: whether endogeneity is really present, whether it matters and whether the selected instruments make sense. Using Bayesian methods, we find that the relationship between education and entrepreneurial income is indeed endogenous and that the impact of endogeneity on the estimated relationship between educa-tion and income is sizeable. We do so using family background variables and show that relaxing the strict validity assumption of these instruments does not lead to strongly different results. This is an important finding because family background variables are generally strongly correlated with education and are available in most datasets. Our approach is applicable beyond the field of returns to education for income. It applies wherever endogeneity suspicion arises and the three questions become relevant

Work Experience from Paid Employment and the Path to Entrepreneurship: Business Takeover versus New Venture Start-Up

Our paper investigates how the type of work experience gained from prior paid employment influences the path to entrepreneurship. We distinguish between two distinct entrepreneurship entry modes: business takeover and new venture start-up. Using a large and rich French data set, we find that small firm experience increases the likelihood for business takeover, whereas management and same sector experience both increase the likelihood for new ventures. Our findings are relevant for policymakers aiming to improve the business transfer process

Phenomenological analysis connecting proton-proton and antiproton-proton elastic scattering

Based on the behavior of the elastic scattering data, we introduce an almost model-independent parametrization for the imaginary part of the scattering amplitude, with the energy and momentum transfer dependences inferred on empirical basis and selected by rigorous theorems and bounds from axiomatic quantum field theory. The corresponding real part is analytically evaluated by means of dispersion relations, allowing connections between particle-particle and particle-antiparticle scattering. Simultaneous fits to proton-proton and antiproton-proton experimental data in the forward direction and also including data beyond the forward direction, lead to a predictive formalism in both energy and momentum transfer. We compare our extrapolations with predictions from some popular models and discuss the applicability of the results in the normalization of elastic rates that can be extracted from present and future accelerator experiments (Tevatron, RHIC and LHC).Comment: 17 pages, 17 figures, to appear in Eur. Phys. J.